**4. Conclusions**

In all forecasting problems, input parameter selection is of grea<sup>t</sup> importance. The developed methodology in the current research proposes a solution to the trial-and-error approach employed by previous research in a demand forecasting field. The developed approach is compatible with any given dataset and can perform in cases of addition or removal of input variables. Assigning a multi-layer perceptron neural network as the output of NSGAII makes it possible to realize secondary processes automatically which, in the case of the current research, is using the ANFIS model to improve forecasting capability of the MLPNN.

Regarding processing time and computational complexity, since the most complex process is finding the input vector, once inputs are selected and the output of the NSGAII is generated, the algorithm can be used for online applications. It will be necessary to run the primary forecast part of the algorithm just in case of availability of new parameters, which can increase the forecasting accuracy.

According to the obtained results, while MLPNN has a better forecasting accuracy compared to the ANFIS, the combination of these two models reduces all forecasting error indicators. In addition, meta-heuristic algorithms are found to be suitable for training of the ANFIS. GA demonstrated better performance in terms of ANFIS training compared to the hybrid method. Among all tested models, the MLPNN-ANFIS-GA model presented lower error rates in terms of the RMSE, MAE, MAPE, and R.

**Author Contributions:** A.J. developed the methodology, analyzed data and generated results. R.M. and N.d.S. edited and re-wrote the manuscript drafts, and participated in generating results. A.C.d.C.L. supervised the research and approved the submitted manuscript.

**Funding:** This research has been funded by the Coordination for the Improvement of Higher Education Personnel (CAPES).

**Conflicts of Interest:** The authors declare no conflict of interest.
